Abstract 1938: Large-scale identification of mutation activity and drug sensitivity in BRAF via a novel multi-label multi-task CNN model
Authors: Ilona Kifer, Arie Aizenman, Natalie Fillipov-Levy, Zohar Barbash, Michael Vidne, Gabi Tarcic
Journal: Cancer Research (2022)

Abstract Precision medicine has allowed for many drugs to be developed for frequently occurring well studied oncogenic mutations such as NTRK fusions or EGFR exon 19 mutations. However, large scale genomic sequencing of patient samples shows that tumors harbor many mutations classified as Variants of Uncertain Significance (VUS). Such mutations could have a significant role in tumor progression and can thus serve as potential drug targets. Therefore, understanding and characterizing the functional significance of VUSs and their response to targeted agents is essential. Here we present a novel machine learning (ML) model consisting of a multi-label, multi-task deep convolutional neural network followed by a decision tree-based regression model. Focusing on alterations in BRAF, we used data from a cell-based assay that measures the activity of signaling pathway activation. This is done using fluorescent imaging of cells expressing a mutated protein together with a fluorescently labeled signaling pathway reporter, providing the input to our model. We trained the model on 3 types of cell images: cells transfected with WT BRAF, BRAF V600E, and BRAF V600E treated with a high dose of Vemurafenib. We use two datasets to evaluate our performance: a set of 17 known active BRAF fusions, as well as a set of 16 known active non-V600E mutations. We also compare our performance to previously published single-task ML model that aims to detect both activity and response. The two methodologies are compared via two criteria: ability to detect activity of the mutations in the dataset, as well as ability to predict response to Vemurafenib or to FORE8394, a drug previously unseen by the model. We show that while both single-task and multi-task models identify all 17 known active fusions as oncogenic, the multi-task does slightly better on the non-V600E mutations, correctly identifying 15/16 of the active mutations vs 10/16 for the single-task model. Comparing drug response, the multi-task model has higher sensitivity in detecting active mutations as responsive to Vemurafenib or FORE8394, including all V600 mutations which are known responders to Vemurafenib and for whom the single-task model does not capture response. Following the training and validation, given a dataset of &amp;gt;300 previously unseen BRAF mutations, the multi-task model is then able to predict both the mutation’s oncogenicity level as well as its expected response to the given drug. Interestingly, the multi-task model also suggests a different drug response profile for vemurafenib compared to FORE8394. We conclude that our novel multi-task model provides an accurate and efficient method for uncovering the actionable and treatable mutational landscape of a drug for patients with mutations in BRAF. It can thus be viewed as a step forward in developing sensitive methodologies for determining patients that are more susceptible to benefit from a potential drug. Citation Format: Ilona Kifer, Arie Aizenman, Natalie Fillipov-Levy, Zohar Barbash, Michael Vidne, Gabi Tarcic. Large-scale identification of mutation activity and drug sensitivity in BRAF via a novel multi-label multi-task CNN model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1938.